Journal of Food, Agriculture and Environment

Multi-objective optimization of decoloration and lactosucrose recovery through artificial neural network and genetic algorithm

Author(s):

Zhou Yan ¹, Ruan Zheng ^1*, Yin Fugui ², Shu Guan ^{2, 3}, Xiaoli Zhou ¹, Liao Chun-Long ¹, Dai Zhi-Kai ¹

Recieved Date: 2010-07-24, Accepted Date: 2010-11-07

Abstract:

Artificial neural network (ANN) and genetic algorithm (GA) with uniform design (UD) were used to optimize the decoloration and lactosucrose (LS) recovery in solution with granular charcoal. Three input variables (dosage of charcoal, time and temperature) were chosen in constructing the back propagation neural networks (BPNN) model and decoloration rate and LS recovery rate as output variables. GA was used to optimize the input space of the ANN model to find out the Pareto-optimal set. The best parameters were the dosage of charcoal varying from 2.1894 to 2.1897%, time from 64.05 to 64.06 min and temperature from 74.22 to 78.90 °C. The optimal predicted decoloration rate is 96.30% and LS recovery rate is 97.35%. Results from confirmative studies showed that decoloration rate was 94.85% and LS recovery rate was 97.23%, and the relative error of network predicted values and actual measured values were 1.51% and 0.12%, respectively. The result suggested that the UD-ANN-GA could effectively solve the separation efficiency by column chromatography and the method was reliable.

Keywords:

Artificial neural network, genetic algorithms, multi-objective optimization, decoloration, lactosucrose

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Journal: Journal of Food, Agriculture and Environment
Year: 2010
Volume: 8
Issue: 3&4
Category: Food and Health
Pages: 121-127

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